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A community-detection based approach to identification of inhomogeneities in granular matter

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  • Navakas, Robertas
  • Džiugys, Algis
  • Peters, Bernhard

Abstract

Interparticle interactions in granular matter are commonly represented by appropriate graphs, therefore, inhomogeneities in granular matter can be possibly reflected by community structure in the respective graphs. Approaches and algorithms for community detection are being actively developed and the achievements in this area can be utilized for analysis of granular systems, where bridging the gap from microscopic configuration to macroscopic phenomena is of great interest.

Suggested Citation

  • Navakas, Robertas & Džiugys, Algis & Peters, Bernhard, 2014. "A community-detection based approach to identification of inhomogeneities in granular matter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 312-331.
    • Handle: RePEc:eee:phsmap:v:407:y:2014:i:c:p:312-331
    DOI: 10.1016/j.physa.2014.04.003
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    References listed on IDEAS

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    1. Michael T Schaub & Jean-Charles Delvenne & Sophia N Yaliraki & Mauricio Barahona, 2012. "Markov Dynamics as a Zooming Lens for Multiscale Community Detection: Non Clique-Like Communities and the Field-of-View Limit," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    2. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Džiugys, Algis & Mahmoudi, Amir Houshang & Misiulis, Edgaras & Navakas, Robertas & Skarbalius, Gediminas, 2022. "Fractal dependence of the packed bed porosity on the particles size distribution," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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